Assistant Professor in the Statistics Department (by courtesy) at Purdue.

Lawson Building 2142-J, West Lafayette, IN 47907, phone: 765-496-6757

e-mail: jhonorio at purdue.edu

Through a unifying framework, with the power of continuous relaxations and primal-dual certificates, my research group produces novel algorithms for learning and inference in combinatorial problems. Our aim is to generate correct, computationally efficient and statistically efficient algorithms for high dimensional machine learning problems. Our results include algorithms for learning and inference in structured prediction, community detection, learning Bayesian networks and graphical games. [vita]

Prior to joining Purdue, I was a postdoctoral associate at MIT CSAIL, working with Tommi Jaakkola. My Erdős number is 3: Jean Honorio → Tommi Jaakkola → Noga Alon → Paul Erdős.

*Current.*Adarsh Barik, Chuyang Ke, Hanbyul Lee, Wenjie Li, Zhanyu Wang, Xiaochen Yang.*Past.*Kevin Bello, Asish Ghoshal.*Other co-authors.*Donald Adams, Gregory Dexter, Abi Komanduru, Zitao Li, Jiajun Liang, Yuki Ohnishi, Keehwan Park, Qiuling Xu, Qian Zhang.*Other past co-authors.*Abdulrahman Alabdulkareem, Longyun Guo, Krishna Kesari, Yu-Jun Li, Meimei Liu, Raphael Meyer, Zhaosen Wang, Yixi Xu, Yilin Zheng.*Prospective.*Here is a note for students who are considering working with me.

Lee H., Bello K.,

(Under submission.)

A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy. (Preprint)

Bello K., C. Ke,

(Under submission.)

Exact Inference with Latent Variables in an Arbitrary Domain. (Preprint)

Ke C.,

(Under submission.)

Minimax Bounds for Structured Prediction Based on Factor Graphs.

Bello K., Ghoshal A.,

Exact Inference in Structured Prediction.

Bello K.,

Learning Latent Variable Structured Prediction Models with Gaussian Perturbations.

Bello K.,

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time. (Long presentation)

Ghoshal A.,

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms.

Ke C.,

(Under submission.)

Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation. (Preprint)

Ke C.,

(Under submission.)

Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection.

Liang J., Ke C.,

Information-Theoretic Limits for Community Detection in Network Models.

Ke C.,

Information-Theoretic Lower Bounds for Recovery of Diffusion Network Structures.

Park K.,

Barik A.,

Fairness Constraints can Help Exact Inference in Structured Prediction.

Bello K.,

Ke C.,

(Under submission.)

Exact Support Recovery in Federated Regression with One-shot Communication. (Preprint)

Barik A.,

(Under submission.)

Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation.

Zhang Q., Zheng Y.,

The Sample Complexity of Meta Sparse Regression.

Wang Z.,

Li W., Barik A.,

(Under submission.)

Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees.

Dexter G., Bello K.,

Information Theoretic Sample Complexity Lower Bound for Feed-Forward Fully-Connected Deep Networks. (Preprint)

Yang X.,

(Under submission.)

A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning.

Komanduru A.,

On the Correctness and Sample Complexity of Inverse Reinforcement Learning.

Komanduru A.,

Barik A.,

(Under submission.)

Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games. (Preprint)

Barik A.,

(Under submission.)

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity.

Ghoshal A.,

On the Sample Complexity of Learning Graphical Games.

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions.

Ghoshal A.,

From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria.

Ghoshal A.,

Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data.

Ghoshal A.,

(Under submission.)

Provable Efficient Skeleton Learning of Encodable Discrete Bayes Nets in Poly-Time and Sample Complexity.

Barik A.,

Learning Bayesian Networks with Low Rank Conditional Probability Tables.

Barik A.,

Learning Causal Bayes Networks Using Interventional Path Queries in Polynomial Time and Sample Complexity.

Bello K.,

Learning Linear Structural Equation Models in Polynomial Time and Sample Complexity.

Ghoshal A.,

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity.

Ghoshal A.,

Information-Theoretic Limits of Bayesian Network Structure Learning.

Ghoshal A.,

Kesari K.,

Reconstructing a Bounded-Degree Directed Tree Using Path Queries.

Wang Z.,

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models.

Technical report, 2015. [code]

Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models.

Variable Selection for Gaussian Graphical Models.

Lipschitz Parametrization of Probabilistic Graphical Models.

Multi-Task Learning of Gaussian Graphical Models.

Sparse and Locally Constant Gaussian Graphical Models.

Barik A.,

(Under submission.)

A Le Cam Type Bound for Adversarial Learning and Applications.

Bello K., Xu Q.,

Information-Theoretic Bounds for Integral Estimation.

Adams D., Barik A.,

Information-Theoretic Lower Bounds for Zero-Order Stochastic Gradient Estimation.

Alabdulkareem A.,

Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability.

Li Z.,

Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation.

Ohnishi Y.,

Optimality Implies Kernel Sum Classifiers are Statistically Efficient.

Meyer R.,

Cost-Aware Learning for Improved Identifiability with Multiple Experiments.

Guo L.,

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression.

Liu M.,

On the Statistical Efficiency of Compositional Nonparametric Prediction.

Xu Y.,

The Error Probability of Random Fourier Features is Dimensionality Independent. (Preprint)

Li Y.,

Technical report, 2018.

Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity.

Barik A.,

A Unified Framework for Consistency of Regularized Loss Minimizers.

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees.

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.

Widmoser M., Pacheco M.,

PrivSyn: Differentially Private Data Synthesis.

Zhang Z., Wang T.,

Variable Selection in Gaussian Markov Random Fields.

Invited book chapter in

Edited by Aravkin A., Deng L., Heigold G., Jebara T., Kanevski D., Wright S. (to be published on December, 2016.)

Predictive Sparse Modeling of fMRI Data for Improved Classification, Regression, and Visualization Using the k-Support Norm.

Belilovsky E., Gkirtzou K., Misyrlis M., Konova A.,

Integration of PCA with a Novel Machine Learning Method for Reparameterization and Assisted History Matching Geologically Complex Reservoirs.

Classification on Brain Functional Magnetic Resonance Imaging: Dimensionality, Sample Size, Subject Variability and Noise.

Invited book chapter in

Edited by Chen C.,

Improving Interpretability of Graphical Models in fMRI Analysis via Variable-Selection.

Predicting Cross-task Behavioral Variables from fMRI Data Using the k-Support Norm.

Misyrlis M., Konova A., Blaschko M.,

Medical Image Computing and Computer-Assisted Intervention.

Methylphenidate Enhances Executive Function and Optimizes Prefrontal Function in Both Health and Cocaine Addiction.

Moeller S.,

Integration of Principal Component Analysis and Streamline Information for the History Matching of Channelized Reservoirs.

Chen C., Gao G.,

fMRI Analysis of Cocaine Addiction Using k-Support Sparsity.

Gkirtzou K.,

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.

Gkirtzou K.,

Medical Image Computing and Computer-Assisted Intervention,

Can a Single Brain Region Predict a Disorder?

Dopaminergic Involvement During Mental Fatigue in Health and Cocaine Addiction.

Moeller S., Tomasi D.,

Enhanced Midbrain Response at 6-month Follow-up in Cocaine Addiction, Association with Reduced Drug-related Choice.

Moeller S., Tomasi D., Woicik P., Maloney T., Alia-Klein N.,

Two-person Interaction Detection Using Body-Pose Features and Multiple Instance Learning.

Yun K.,

IEEE Computer Vision and Pattern Recognition,

Dopaminergic contribution to endogenous motivation during cognitive control breakdown.

Moeller S., Tomasi D.,

Digital Analysis and Visualization of Swimming Motion.

Kirmizibayrak C.,

Digital Analysis and Visualization of Swimming Motion.

Kirmizibayrak C.,

Conference on Computer Animation and Social Agents,

Simple Fully Automated Group Classification on Brain fMRI.

Disrupted Functional Connectivity with Dopaminergic Midbrain in Cocaine Abusers.

Tomasi D., Volkow N., Wang R.,

Oral Methylphenidate Normalizes Cingulate Activity in Cocaine Addiction During a Salient Cognitive Task.

Goldstein R., Woicik P., Maloney T., Tomasi D., Alia-Klein N., Shan J.,

Learning Brain fMRI Structure Through Sparseness and Local Constancy.

Neural Information Processing Systems,

A Functional Geometry of fMRI BOLD Signal Interactions.

Langs G., Samaras D., Paragios N.,

Neural Information Processing Systems,

Dopaminergic Response to Drug Words in Cocaine Addiction.

Goldstein R., Tomasi D., Alia-Klein N.,

Anterior Cingulate Cortex Hypoactivations to an Emotionally Salient Task in Cocaine Addiction.

Goldstein R., Alia-Klein N., Tomasi D.,

Task-Specific Functional Brain Geometry from Model Maps.

Langs G., Samaras D., Paragios N.,